Meeting Title: MatterMore | internal Standup Date: 2025-06-27 Meeting participants: Awaish Kumar, Luke Daque, Amber Lin
WEBVTT
1 00:00:24.490 ⇒ 00:00:25.390 Luke Daque: Timer.
2 00:00:28.300 ⇒ 00:00:29.280 Amber Lin: Hello!
3 00:00:30.520 ⇒ 00:00:31.410 Luke Daque: How’s it going.
4 00:00:32.610 ⇒ 00:00:40.449 Amber Lin: Very good. Worked not 10 h yesterday, so very happy. I feel a bit more rested.
5 00:00:41.000 ⇒ 00:00:46.999 Amber Lin: How come you were asking about a Pm. Position. Do you know anyone that wants to work as a Pm.
6 00:00:47.820 ⇒ 00:00:48.870 Luke Daque: Oh, yeah, I haven’t.
7 00:00:49.130 ⇒ 00:00:51.880 Luke Daque: I have some, and click this
8 00:00:52.120 ⇒ 00:00:55.280 Luke Daque: Pm. That I used to work with before at Telus.
9 00:00:55.760 ⇒ 00:00:56.300 Amber Lin: Oh!
10 00:00:56.652 ⇒ 00:01:05.120 Luke Daque: He’s pretty decent. He’s he’s great, actually. And like he got he just got recently laid off. So he’s looking for a job. So.
11 00:01:05.120 ⇒ 00:01:05.550 Amber Lin: Oh!
12 00:01:05.550 ⇒ 00:01:07.130 Luke Daque: Like asking me if there’s any
13 00:01:07.290 ⇒ 00:01:12.370 Luke Daque: like open positions or whatever. So yeah, you should tell, make a living.
14 00:01:12.370 ⇒ 00:01:14.449 Amber Lin: He’s looking for someone actually.
15 00:01:15.660 ⇒ 00:01:22.390 Luke Daque: Cool. Yeah, that’d be great. I can send him. I can send you the Linkedin if you want to take a look as well.
16 00:01:23.120 ⇒ 00:01:23.700 Amber Lin: Sure
17 00:01:25.620 ⇒ 00:01:32.840 Amber Lin: about it, and also like, send me the link he cause. Utah will be the one making the decision like I can.
18 00:01:32.840 ⇒ 00:01:33.460 Luke Daque: Gotcha.
19 00:01:33.460 ⇒ 00:01:37.230 Amber Lin: Look at it. But I can’t say like, Okay, we’re gonna hire this person.
20 00:01:38.420 ⇒ 00:01:39.920 Luke Daque: Cool sounds, good.
21 00:01:40.260 ⇒ 00:01:40.910 Amber Lin: Yeah.
22 00:01:43.470 ⇒ 00:01:43.950 Luke Daque: Nice.
23 00:01:45.190 ⇒ 00:01:47.940 Amber Lin: Let me see if a wish can join.
24 00:02:14.320 ⇒ 00:02:24.310 Amber Lin: Yeah, let me share my screen. We can talk real quick about any of the tickets.
25 00:02:25.530 ⇒ 00:02:26.450 Luke Daque: Sure.
26 00:02:28.481 ⇒ 00:02:40.009 Amber Lin: I guess just everything yesterday was, were you able to have time to? Sorry? Not that one. Were you able to have time to look at parts of this, and.
27 00:02:40.010 ⇒ 00:02:44.399 Luke Daque: Yeah, yeah, but I haven’t like
28 00:02:44.520 ⇒ 00:02:49.679 Luke Daque: made an update yet. So there’s still unknowns at the moment. But yeah, I can. I can.
29 00:02:49.910 ⇒ 00:02:54.440 Luke Daque: I’ll work on that today so that we can have.
30 00:02:55.430 ⇒ 00:02:55.800 Amber Lin: Okay.
31 00:02:55.800 ⇒ 00:02:57.470 Luke Daque: Those fields. They say something.
32 00:02:58.530 ⇒ 00:03:03.680 Amber Lin: Okay. Sounds good. Yeah, that was every that was everything for this brand.
33 00:03:04.311 ⇒ 00:03:09.509 Amber Lin: I’ll work with a ways to check on the tickets in the backlog, and
34 00:03:09.850 ⇒ 00:03:12.590 Amber Lin: next Monday we’ll kick off the next spread.
35 00:03:14.270 ⇒ 00:03:15.360 Luke Daque: Okay. Sounds good.
36 00:03:15.670 ⇒ 00:03:19.990 Amber Lin: Yeah, thanks, Luke. Yeah, that’s the only one. I don’t know how long that would take.
37 00:03:21.430 ⇒ 00:03:24.650 Luke Daque: Yeah, that should. I should be able to finish that by today.
38 00:03:25.000 ⇒ 00:03:26.669 Amber Lin: Okay. Awesome.
39 00:03:28.960 ⇒ 00:03:33.390 Luke Daque: Cool. Yeah, I was just here in case you need anything else.
40 00:03:35.305 ⇒ 00:03:42.869 Amber Lin: Okay, I, wish we can look at
41 00:03:43.520 ⇒ 00:03:52.880 Amber Lin: these together. I groomed the backlog as best as I could with AI so get to give you an overview. These 3
42 00:03:53.250 ⇒ 00:03:57.390 Amber Lin: are from the client. So we don’t need to worry about those.
43 00:03:59.180 ⇒ 00:04:04.810 Amber Lin: These are essentially everything left that we that I know of that we need to do.
44 00:04:04.950 ⇒ 00:04:08.490 Amber Lin: and these ones are not as
45 00:04:08.990 ⇒ 00:04:17.570 Amber Lin: urgent. So they’re either further segments or related to connecting to client data which we don’t have a
46 00:04:17.920 ⇒ 00:04:22.049 Amber Lin: good answer on and so
47 00:04:22.250 ⇒ 00:04:24.679 Amber Lin: here, what I put in here is
48 00:04:25.550 ⇒ 00:04:30.230 Amber Lin: so the top few. So these are
49 00:04:32.950 ⇒ 00:04:44.530 Amber Lin: sorry. These 3 are additional requirements or additions that I got as I met with a client stakeholder yesterday, she was like, oh, we want these extra few things.
50 00:04:44.890 ⇒ 00:04:51.160 Amber Lin: So I put that down as tickets. They’re mostly powered. Bi. Well, they’re all power bi items.
51 00:04:51.340 ⇒ 00:04:55.340 Amber Lin: And then I have
52 00:04:57.560 ⇒ 00:05:00.080 Amber Lin: And then we have these
53 00:05:00.550 ⇒ 00:05:20.620 Amber Lin: because we are pretty much done with these segments, but we still have the 3.rd These few segments that we can do. I tried to confirm with client. They were like, Oh, I don’t know. Maybe let me go check. So not only included 2, but we probably I only put them as low priority.
54 00:05:22.150 ⇒ 00:05:23.330 Amber Lin: and then last one
55 00:05:24.020 ⇒ 00:05:45.770 Amber Lin: session duration which we were talking about last time. And I think we’re we’re in a good position to do that, because Luke was able to get all of the audit log data, and that gives us a pretty clear plan on what to do. I just want us to confirm that I’ll go confirm with clients with with any assumptions we have, and we can go ahead and model.
56 00:05:46.600 ⇒ 00:05:51.416 Amber Lin: So I guess I wanna hear from you of how
57 00:05:53.090 ⇒ 00:05:55.149 Amber Lin: how? What you think of this plan.
58 00:05:57.990 ⇒ 00:06:01.089 Awaish Kumar: Yeah, like it. It looks good like we just need to.
59 00:06:01.800 ⇒ 00:06:02.910 Awaish Kumar: Okay, you have.
60 00:06:03.240 ⇒ 00:06:05.679 Awaish Kumar: You have already assigned the story points.
61 00:06:06.770 ⇒ 00:06:09.629 Amber Lin: Right, they might not be accurate.
62 00:06:16.190 ⇒ 00:06:22.550 Awaish Kumar: Present plan for session. Duration like, is it? This is on you like, can we assign these tickets.
63 00:06:23.400 ⇒ 00:06:24.350 Amber Lin: Oh, true!
64 00:06:26.960 ⇒ 00:06:30.749 Awaish Kumar: So let’s like like, let’s assume, then we
65 00:06:31.070 ⇒ 00:06:40.710 Awaish Kumar: like, maybe cross the points. And then finally, you can see, like what we have for the week, and if if it’s enough.
66 00:06:41.890 ⇒ 00:06:50.670 Amber Lin: Yeah, yeah, I agree. Okay, this one, is for.
67 00:06:50.670 ⇒ 00:06:52.090 Awaish Kumar: This one is assigned to you.
68 00:06:52.460 ⇒ 00:07:04.220 Amber Lin: This few segments. That one will be me. AI already gave me a plan. I can confirm with you guys as once we get to these tickets, we’ll essentially get them confirmed.
69 00:07:04.490 ⇒ 00:07:07.969 Amber Lin: I just need to tell clients, hey, these. This is what we’re doing
70 00:07:08.210 ⇒ 00:07:13.589 Amber Lin: like. This is more of a I need to send a slack message and writing. Okay.
71 00:07:13.590 ⇒ 00:07:14.690 Awaish Kumar: Yeah, that’s okay.
72 00:07:14.960 ⇒ 00:07:15.630 Awaish Kumar: Hmm.
73 00:07:15.810 ⇒ 00:07:16.220 Amber Lin: Yes.
74 00:07:16.220 ⇒ 00:07:18.470 Awaish Kumar: Yeah. Just just let me know if you need anything.
75 00:07:18.990 ⇒ 00:07:23.859 Amber Lin: Of course. Yeah, I will ask you guys for the plan. But this is something I need to do.
76 00:07:24.090 ⇒ 00:07:29.280 Amber Lin: So same for this. I need to ask you guys, okay, are we good with this plan?
77 00:07:30.680 ⇒ 00:07:36.619 Amber Lin: And then tell clients hey this is our plan and do you have objections and then that is
78 00:07:37.065 ⇒ 00:07:38.260 Amber Lin: alright, we are.
79 00:07:38.820 ⇒ 00:07:39.989 Awaish Kumar: Oh, yeah. Penny.
80 00:07:40.490 ⇒ 00:07:44.770 Amber Lin: Yeah. The problem is, Annie is on vacation until
81 00:07:45.010 ⇒ 00:07:51.830 Amber Lin: the 3, rd I think. Let me check real quick. Her oh.
82 00:07:51.830 ⇒ 00:07:53.749 Awaish Kumar: But like we don’t have a.
83 00:07:54.210 ⇒ 00:07:54.890 Amber Lin: You can.
84 00:07:54.890 ⇒ 00:08:01.029 Awaish Kumar: Someone else like like. Put them, said he. He might give a hand, but like I’m not.
85 00:08:01.030 ⇒ 00:08:05.690 Amber Lin: I don’t think you’ll have time. Annie says she’ll be on vacation till the 3.rd
86 00:08:06.070 ⇒ 00:08:08.518 Amber Lin: So that’s a Thursday.
87 00:08:09.870 ⇒ 00:08:21.430 Amber Lin: I was thinking, I said, I told the clients, hey, hey! We need a full sprint. So if they’re okay with it, we can. We can do this the second week
88 00:08:21.990 ⇒ 00:08:23.850 Amber Lin: of the sprint.
89 00:08:24.270 ⇒ 00:08:35.910 Amber Lin: If not maybe we can do one of the items or one of the more simple items I maybe look, you could help, or I could look into. How to do that like this one is
90 00:08:36.270 ⇒ 00:08:37.969 Amber Lin: a really simple one.
91 00:08:39.233 ⇒ 00:08:52.670 Amber Lin: The client just saw this page, and then they wanted the same one for weekend. So I know we already have that modeled it. Just we can duplicate the page and select the the data.
92 00:08:54.205 ⇒ 00:08:56.850 Luke Daque: What last weekend.
93 00:08:57.410 ⇒ 00:09:04.359 Amber Lin: Yeah, currently, we have the weekend low by segment. Right? They want to see it weekday. So.
94 00:09:05.280 ⇒ 00:09:05.700 Luke Daque: Thanks.
95 00:09:05.700 ⇒ 00:09:07.009 Amber Lin: Want to see if we’re.
96 00:09:07.010 ⇒ 00:09:07.990 Awaish Kumar: As well. Yeah.
97 00:09:10.100 ⇒ 00:09:13.259 Amber Lin: Yeah, I don’t think that will take very long.
98 00:09:13.920 ⇒ 00:09:30.049 Amber Lin: especially, I think, when I when I saw the models, we already have the days classified as okay. Saturday and Sunday are gonna be like weekend. So I think we already have. We’re ready to do this. I just don’t know who to assign it to, because Annie is off.
99 00:09:34.740 ⇒ 00:09:44.400 Awaish Kumar: Remember like for now, like maybe assign to add like any everybody picks up after
100 00:09:44.400 ⇒ 00:09:46.089 Awaish Kumar: more like we can see.
101 00:09:46.510 ⇒ 00:09:47.100 Amber Lin: Great.
102 00:09:47.100 ⇒ 00:09:48.200 Awaish Kumar: So, yeah.
103 00:09:49.020 ⇒ 00:09:57.850 Amber Lin: Okay, so these are all power bi requirements. I can flush it out. So this one, you know, we currently have it.
104 00:09:58.120 ⇒ 00:09:59.920 Amber Lin: Say, for this.
105 00:10:00.260 ⇒ 00:10:04.369 Amber Lin: If we select multiple teams or
106 00:10:04.720 ⇒ 00:10:15.680 Amber Lin: multiple roles, it’s so it just combines it into one bar. And I think the client also wants to be able to see for different teams the side by side bars.
107 00:10:16.120 ⇒ 00:10:17.390 Amber Lin: If that makes sense.
108 00:10:17.390 ⇒ 00:10:18.000 Awaish Kumar: Okay.
109 00:10:18.390 ⇒ 00:10:18.990 Amber Lin: Yeah.
110 00:10:19.190 ⇒ 00:10:22.519 Amber Lin: So that’s that’s this ticket.
111 00:10:23.290 ⇒ 00:10:24.779 Amber Lin: And then this one.
112 00:10:27.704 ⇒ 00:10:28.260 Amber Lin: Sorry.
113 00:10:30.640 ⇒ 00:10:34.190 Awaish Kumar: For a dimension selector, for pages.
114 00:10:34.330 ⇒ 00:10:45.010 Amber Lin: I think, for that one. What they mean is they wanna be able to. I only already had this. It was awesome like to be able to choose what goes on the X-axis
115 00:10:45.790 ⇒ 00:10:53.000 Amber Lin: like. They just want to be able to have this for these 2 as well.
116 00:10:53.000 ⇒ 00:10:53.510 Awaish Kumar: Okay.
117 00:10:54.720 ⇒ 00:10:57.440 Amber Lin: Yeah, let me actually take a screenshot.
118 00:11:10.870 ⇒ 00:11:13.440 Amber Lin: Okay, that’s that.
119 00:11:17.530 ⇒ 00:11:18.460 Amber Lin: And then.
120 00:11:18.460 ⇒ 00:11:19.389 Awaish Kumar: So funny.
121 00:11:20.710 ⇒ 00:11:21.730 Amber Lin: Miss one.
122 00:11:26.230 ⇒ 00:11:30.609 Amber Lin: So this is the modeling task. I think I put
123 00:11:31.500 ⇒ 00:11:35.419 Amber Lin: a few ways we can do it in here.
124 00:11:36.030 ⇒ 00:11:38.359 Amber Lin: I think we should go here. So it’s
125 00:11:38.680 ⇒ 00:11:43.910 Amber Lin: right. Now we have all the audit log data, which is.
126 00:11:43.910 ⇒ 00:11:44.360 Awaish Kumar: Certainly.
127 00:11:44.360 ⇒ 00:11:47.040 Amber Lin: Event times for different tools, right?
128 00:11:47.250 ⇒ 00:11:51.120 Amber Lin: And from the event times, maybe like
129 00:11:52.340 ⇒ 00:11:59.650 Amber Lin: (101) 030-1035. Each of those have a different event. And then we can infer, okay.
130 00:11:59.770 ⇒ 00:12:04.679 Amber Lin: when did a user finish using this tool? When did a user switch their tools?
131 00:12:04.990 ⇒ 00:12:07.750 Amber Lin: So we can have modeling around that.
132 00:12:09.000 ⇒ 00:12:14.240 Awaish Kumar: But like for the session like like it, it
133 00:12:14.900 ⇒ 00:12:19.169 Awaish Kumar: like I necessarily like. It’s not necessary for me to like
134 00:12:19.610 ⇒ 00:12:25.929 Awaish Kumar: switch the tool like I can log out. I can just close the tab. I can close the browser.
135 00:12:28.480 ⇒ 00:12:33.199 Awaish Kumar: And other. Also I can stay this
136 00:12:35.970 ⇒ 00:12:40.180 Awaish Kumar: tab, like in the onedrive. But it’s it goes like session
137 00:12:40.490 ⇒ 00:12:48.970 Awaish Kumar: gets out of it like a session gets let’s say expired after some time, and then oh!
138 00:12:49.140 ⇒ 00:12:55.860 Awaish Kumar: And I then again do some activity in the file, so.
139 00:12:56.680 ⇒ 00:12:59.959 Awaish Kumar: Like like, why, like shift the tone
140 00:13:00.580 ⇒ 00:13:03.949 Awaish Kumar: and like it like it might be
141 00:13:05.030 ⇒ 00:13:08.289 Awaish Kumar: one of the indicators that okay, now we are on some other
142 00:13:08.430 ⇒ 00:13:12.469 Awaish Kumar: tool, but it’s like the in the other places where
143 00:13:12.680 ⇒ 00:13:19.830 Awaish Kumar: a person doesn’t switch from like Microsoft tools goes on some excel sheet or something like that like?
144 00:13:20.120 ⇒ 00:13:21.319 Awaish Kumar: Then we don’t know.
145 00:13:22.480 ⇒ 00:13:23.260 Amber Lin: Yeah.
146 00:13:23.850 ⇒ 00:13:27.210 Amber Lin: Now that I guess that’s why I kind of wanted to talk with you guys on
147 00:13:27.520 ⇒ 00:13:33.260 Amber Lin: how we actually let me assign this to you like how we are going to do this.
148 00:13:35.220 ⇒ 00:13:36.819 Luke Daque: Yeah, that might be.
149 00:13:36.820 ⇒ 00:13:38.130 Awaish Kumar: Yeah. Like, oh, okay.
150 00:13:38.130 ⇒ 00:13:38.550 Luke Daque: Boy.
151 00:13:38.550 ⇒ 00:13:39.660 Awaish Kumar: It will be.
152 00:13:44.920 ⇒ 00:13:50.139 Awaish Kumar: yeah. But like, that’s the if we keep it simple by timing like, if we say.
153 00:13:50.290 ⇒ 00:13:54.039 Awaish Kumar: if a person’s activity on. What like if someone
154 00:13:54.950 ⇒ 00:14:08.260 Awaish Kumar: like, if someone switches tool, what means the activity like from onedrive to teams? Profile? Right? So it means the activity of that same person on the onedrive is
155 00:14:09.050 ⇒ 00:14:10.790 Awaish Kumar: kind of like
156 00:14:12.780 ⇒ 00:14:20.219 Awaish Kumar: like is is not happening like he. He is not doing any activity right now. He’s on teams.
157 00:14:20.530 ⇒ 00:14:23.024 Awaish Kumar: So that means he’s kind of
158 00:14:24.835 ⇒ 00:14:29.080 Awaish Kumar: moved away from onedrive, and we can end the session.
159 00:14:29.910 ⇒ 00:14:35.499 Awaish Kumar: There like one like I. I think the best solution is
160 00:14:36.875 ⇒ 00:14:43.350 Awaish Kumar: the keeping track of both. So like if we see that drop in activity
161 00:14:44.271 ⇒ 00:14:48.120 Awaish Kumar: is now like the is is
162 00:14:48.440 ⇒ 00:15:03.890 Awaish Kumar: user has haven’t done any activity on one drive for 30 min. Okay? And then it comes back on onedrive. It’s a new session. That’s 1 of the indicator. Second indicator is that like he switches a tool and I’m
163 00:15:04.599 ⇒ 00:15:06.750 Awaish Kumar: it should be there because
164 00:15:06.950 ⇒ 00:15:23.099 Awaish Kumar: it’s possible that, like within 5 min, you go to teams. Right? So our our 30 min logic will fail in that case, because user is no longer on on one drive, but we are still saying it’s onedrive. So we
165 00:15:23.260 ⇒ 00:15:29.259 Awaish Kumar: keep that mind. Keep that in mind like so like switching tool.
166 00:15:30.480 ⇒ 00:15:45.989 Awaish Kumar: can we? One of the indicators. Second, like second condition is that he has not switched any tool to some other like known tool like Microsoft Tool. But like he’s missing for 30 min. Then it’s like we just end the session. There.
167 00:15:50.580 ⇒ 00:15:58.190 Awaish Kumar: Yeah, for the non Microsoft. We don’t know. We want to like from the synthetic data, like from the data, we will only know.
168 00:15:58.190 ⇒ 00:16:01.560 Amber Lin: Yeah, we won’t get my tools, but.
169 00:16:01.560 ⇒ 00:16:02.010 Awaish Kumar: Yeah.
170 00:16:02.010 ⇒ 00:16:08.959 Amber Lin: I I guess we just assume that the clients uses everything, Microsoft like we’ll just. We’ll just hope that that is the case.
171 00:16:09.440 ⇒ 00:16:13.207 Awaish Kumar: Yeah, like, even if he uses Microsoft like, he can just
172 00:16:13.850 ⇒ 00:16:24.039 Awaish Kumar: go to the like excel sheet placed on his computer. Maybe text editor, or something like.
173 00:16:24.260 ⇒ 00:16:24.640 Amber Lin: Which.
174 00:16:24.980 ⇒ 00:16:29.720 Awaish Kumar: Which is not logged in like on the Microsoft graph. Api.
175 00:16:29.720 ⇒ 00:16:30.480 Amber Lin: Okay.
176 00:16:34.510 ⇒ 00:16:35.350 Amber Lin: Great.
177 00:16:35.670 ⇒ 00:16:36.490 Amber Lin: Okay.
178 00:16:36.490 ⇒ 00:16:41.660 Awaish Kumar: So inactivity, so like switching tool, and the inactivity, like combination of these 2.
179 00:16:43.600 ⇒ 00:16:45.939 Amber Lin: How do we account for multitasking
180 00:16:47.020 ⇒ 00:16:50.340 Amber Lin: like? If I have a split tab and I have 2 things, open.
181 00:16:55.300 ⇒ 00:16:56.270 Awaish Kumar: Oh.
182 00:16:59.300 ⇒ 00:17:03.129 Awaish Kumar: okay. But like that is still the
183 00:17:04.190 ⇒ 00:17:07.970 Awaish Kumar: act like, if you are doing any activity on one of the tab
184 00:17:10.119 ⇒ 00:17:14.919 Awaish Kumar: like, like on one side you have onedrive on the other side you have teams.
185 00:17:17.630 ⇒ 00:17:24.060 Awaish Kumar: Now, the session should go where you are active. So if you are on the zoom,
186 00:17:26.020 ⇒ 00:17:27.900 Awaish Kumar: then basically, we have
187 00:17:28.170 ⇒ 00:17:34.800 Awaish Kumar: like activity logs right? So if, and of the zoom, so we know that person is in here.
188 00:17:35.880 ⇒ 00:17:39.569 Awaish Kumar: And at the same time like, if you are also on the
189 00:17:39.680 ⇒ 00:17:45.790 Awaish Kumar: onedrive, we have an act. We will have activity, logs of onedrive, and how we should
190 00:17:47.320 ⇒ 00:18:03.560 Awaish Kumar: so like, it’s kind of tool switching. Right? You we will get that in tool switched right? You are on one drive. You selected a file and you went to the teams and then that like. Now that activity, we get an event for.
191 00:18:03.650 ⇒ 00:18:04.250 Amber Lin: Okay.
192 00:18:04.250 ⇒ 00:18:05.690 Awaish Kumar: School is switched right.
193 00:18:06.060 ⇒ 00:18:06.710 Amber Lin: Okay.
194 00:18:11.980 ⇒ 00:18:12.750 Amber Lin: great.
195 00:18:12.940 ⇒ 00:18:14.810 Amber Lin: I will take this transcript.
196 00:18:15.310 ⇒ 00:18:19.609 Amber Lin: I’ll write out something more detailed with linear.
197 00:18:20.130 ⇒ 00:18:23.860 Amber Lin: and then we can discuss it together again.
198 00:18:26.520 ⇒ 00:18:34.140 Amber Lin: and then we’ll like we’ll flesh out this ticket based on the plans, so I won’t. We don’t have to look at this for now.
199 00:18:34.620 ⇒ 00:18:36.369 Amber Lin: But that will be Luke.
200 00:18:37.330 ⇒ 00:18:38.440 Amber Lin: No!
201 00:18:38.620 ⇒ 00:18:40.570 Amber Lin: Oh, hang on it! Says.
202 00:18:42.120 ⇒ 00:18:43.100 Awaish Kumar: So
203 00:18:51.100 ⇒ 00:18:53.516 Awaish Kumar: so like for this sprint.
204 00:18:54.460 ⇒ 00:18:58.859 Awaish Kumar: so this cycle means what like? It’s a 1 week cycle, or we are.
205 00:18:59.110 ⇒ 00:19:00.240 Amber Lin: 2 weeks.
206 00:19:00.240 ⇒ 00:19:01.820 Awaish Kumar: 2 week cycle.
207 00:19:02.270 ⇒ 00:19:08.720 Awaish Kumar: It’s 2 weeks so far recruit. We kind of.
208 00:19:09.680 ⇒ 00:19:14.949 Awaish Kumar: Even if we make a plan over the weekend, we will have only one task for Luke.
209 00:19:16.350 ⇒ 00:19:19.360 Awaish Kumar: which is like the 3 4 hourly.
210 00:19:25.900 ⇒ 00:19:32.700 Amber Lin: maybe Luke and help on power bi a bit. I know, Luke, you worked on power bi a while a long time ago.
211 00:19:35.260 ⇒ 00:19:36.260 Luke Daque: Yeah, sure.
212 00:19:39.530 ⇒ 00:19:41.419 Awaish Kumar: So like would you like?
213 00:19:41.660 ⇒ 00:19:45.943 Awaish Kumar: Do you feel, Luke? Do you feel confident, picking up on these
214 00:19:46.660 ⇒ 00:19:50.220 Awaish Kumar: all of these power bi task, or one of the.
215 00:20:00.240 ⇒ 00:20:00.960 Luke Daque: Hello!
216 00:20:01.160 ⇒ 00:20:06.799 Awaish Kumar: Hello. Hmm, yeah. I was asking Luke like, How do you feel?
217 00:20:12.280 ⇒ 00:20:14.100 Awaish Kumar: Okay? Can you hear me?
218 00:20:14.100 ⇒ 00:20:17.509 Luke Daque: Or is it a ways? Yeah, I can hear now.
219 00:20:18.090 ⇒ 00:20:18.892 Amber Lin: I hear you.
220 00:20:20.580 ⇒ 00:20:21.280 Awaish Kumar: Okay.
221 00:20:21.410 ⇒ 00:20:29.649 Awaish Kumar: I was asking Luke that like if he based on his experience with power pi, if he feels confident
222 00:20:29.830 ⇒ 00:20:33.439 Awaish Kumar: in working on all of these power Bi tickets.
223 00:20:33.680 ⇒ 00:20:38.129 Awaish Kumar: or any one of the which is simpler. One or.
224 00:20:39.760 ⇒ 00:20:41.210 Luke Daque: Yeah. Sure. That should be fine.
225 00:20:46.030 ⇒ 00:20:49.879 Amber Lin: Okay, I guess. So.
226 00:20:49.880 ⇒ 00:20:52.040 Luke Daque: For the side by side bars which
227 00:20:52.320 ⇒ 00:20:55.600 Luke Daque: which dashboard are we referring to here?
228 00:20:57.940 ⇒ 00:20:59.970 Amber Lin: I think they want it on
229 00:21:02.930 ⇒ 00:21:07.289 Amber Lin: at least this day of week and hour of day
230 00:21:09.930 ⇒ 00:21:16.350 Luke Daque: Gotcha. And then what like do we need to add?
231 00:21:16.860 ⇒ 00:21:22.999 Luke Daque: Oh, so I guess it’s all 3 right. The average events for users or average hours and average
232 00:21:24.650 ⇒ 00:21:25.420 Luke Daque: theme.
233 00:21:27.280 ⇒ 00:21:29.080 Amber Lin: Or do you mean.
234 00:21:32.190 ⇒ 00:21:37.039 Luke Daque: I mean the site like it’s currently showing average minutes per user.
235 00:21:37.300 ⇒ 00:21:43.586 Luke Daque: But they want the side by side bars for all 3 measures like.
236 00:21:44.070 ⇒ 00:21:45.439 Amber Lin: Sorry I meant that.
237 00:21:45.960 ⇒ 00:21:51.480 Amber Lin: So right now, when we select let me go here.
238 00:21:52.020 ⇒ 00:21:56.479 Amber Lin: So when we set select, let’s say 2 of them.
239 00:21:57.060 ⇒ 00:22:16.019 Amber Lin: or if I select one more, it just combines it, it combine. I guess it doesn’t combine. I guess it’s it’s like I can’t compare. They were complaining that they can’t compare side by side, because I don’t think anyone can directly remember all of these. I kind of want to be able to
240 00:22:16.400 ⇒ 00:22:32.989 Amber Lin: put them side by side and compare. And honestly, I wanted this one, if we can, to wait for Annie, because if she’s gonna continue working on power Bi. If we do anything she she might have a certain way she likes to do it, and she might have to change it again.
241 00:22:33.430 ⇒ 00:22:34.080 Amber Lin: So I was.
242 00:22:34.080 ⇒ 00:22:34.940 Luke Daque: Right.
243 00:22:34.940 ⇒ 00:22:48.909 Amber Lin: That we do this plan. Sorry. Do this plan as soon as possible and do, and we get a quick test of how things can be and I guess we could also do this more simple ticket.
244 00:22:52.380 ⇒ 00:22:53.439 Amber Lin: What it is.
245 00:22:53.580 ⇒ 00:22:54.319 Luke Daque: You can’t.
246 00:22:55.540 ⇒ 00:22:56.390 Luke Daque: Okay.
247 00:22:56.700 ⇒ 00:23:00.510 Luke Daque: Dimension is electrical. Field pages.
248 00:23:00.740 ⇒ 00:23:07.209 Amber Lin: Yeah, cause that’s just adding this one what they have here.
249 00:23:07.210 ⇒ 00:23:07.860 Luke Daque: Sync.
250 00:23:08.300 ⇒ 00:23:09.760 Amber Lin: To the different ones.
251 00:23:11.430 ⇒ 00:23:12.100 Luke Daque: Okay.
252 00:23:12.100 ⇒ 00:23:12.850 Amber Lin: That’s all.
253 00:23:15.250 ⇒ 00:23:16.600 Luke Daque: Sure I can do that.
254 00:23:18.250 ⇒ 00:23:25.320 Amber Lin: Yeah. So I would say, this one is. Next, I’ll say, these are for next Friday.
255 00:23:31.080 ⇒ 00:23:36.420 Amber Lin: I’ll say that if we’re in next Friday.
256 00:23:41.920 ⇒ 00:23:51.250 Amber Lin: Yeah, these 2. I need confirmation from the client. So I would say, this, this
257 00:23:51.860 ⇒ 00:23:57.019 Amber Lin: won’t be for end of next cycle.
258 00:23:59.470 ⇒ 00:24:05.160 Amber Lin: and then this one. We’ll try to get started next week, but I don’t know if we’ll get to finish it.
259 00:24:12.760 ⇒ 00:24:16.290 Amber Lin: Okay, is this good.
260 00:24:17.720 ⇒ 00:24:18.280 Luke Daque: Yep.
261 00:24:20.170 ⇒ 00:24:21.210 Amber Lin: Okay, let me drop.
262 00:24:21.210 ⇒ 00:24:22.380 Luke Daque: Maybe just us.
263 00:24:22.620 ⇒ 00:24:23.120 Amber Lin: Yeah, I
264 00:24:29.000 ⇒ 00:24:38.789 Amber Lin: okay, sounds good. If any of those tickets feel unclear. Ping me and I will add, I will flush them out.
265 00:24:41.200 ⇒ 00:24:41.810 Luke Daque: Cool.
266 00:24:42.630 ⇒ 00:24:47.419 Amber Lin: Okay, I wish anything else you want. We wanted to look at.
267 00:24:52.490 ⇒ 00:24:55.499 Awaish Kumar: Sorry. I don’t have anything.
268 00:24:55.830 ⇒ 00:24:57.690 Amber Lin: Okay, sounds good.
269 00:24:57.950 ⇒ 00:24:58.939 Amber Lin: Thank you. Everybody.
270 00:24:58.940 ⇒ 00:24:59.415 Awaish Kumar: Alright,
271 00:24:59.890 ⇒ 00:25:01.069 Amber Lin: I’ll see you next week.
272 00:25:02.470 ⇒ 00:25:02.940 Awaish Kumar: See you.
273 00:25:02.940 ⇒ 00:25:03.560 Luke Daque: See you.
274 00:25:03.560 ⇒ 00:25:04.910 Amber Lin: Yeah, bye-bye.
275 00:25:05.540 ⇒ 00:25:06.260 Luke Daque: Bye.